trainy-skypilot-nightly

Pre-release

This release is a pre-release and may not be stable for production use.

Trainy Logo

This repository is a fork of the original Skypilot and maintained by Trainy in order to support running jobs Trainy's our managed Kubernetes cluster platform as a service, Konduktor (Github and Documentation). You can see some our contributions to the mainline project here. If there are features in this fork you feel like make sense to contribute back to upstream, please let us know and we are happy to make a pull request.

SkyPilot

Run AI on Any Infra — Unified, Faster, Cheaper

🔥 News 🔥

[Oct 2024] 🎉 SkyPilot crossed 1M+ downloads 🎉: Thank you to our community! Twitter/X
[Sep 2024] Point, Launch and Serve Llama 3.2 on Kubernetes or Any Cloud: example
[Sep 2024] Run and deploy Pixtral, the first open-source multimodal model from Mistral AI.
[Jun 2024] Reproduce GPT with llm.c on any cloud: guide
[Apr 2024] Serve Qwen-110B on your infra: example
[Apr 2024] Using Ollama to deploy quantized LLMs on CPUs and GPUs: example
[Feb 2024] Deploying and scaling Gemma with SkyServe: example
[Feb 2024] Serving Code Llama 70B with vLLM and SkyServe: example
[Dec 2023] Mixtral 8x7B, a high quality sparse mixture-of-experts model, was released by Mistral AI! Deploy via SkyPilot on any cloud: example
[Nov 2023] Using Axolotl to finetune Mistral 7B on the cloud (on-demand and spot): example

LLM Finetuning Cookbooks: Finetuning Llama 2 / Llama 3.1 in your own cloud environment, privately: Llama 2 example and blog; Llama 3.1 example and blog

Archived

[Jul 2024] Finetune and serve Llama 3.1 on your infra
[Apr 2024] Serve and finetune Llama 3 on any cloud or Kubernetes: example
[Mar 2024] Serve and deploy Databricks DBRX on your infra: example
[Feb 2024] Speed up your LLM deployments with SGLang for 5x throughput on SkyServe: example
[Dec 2023] Using LoRAX to serve 1000s of finetuned LLMs on a single instance in the cloud: example
[Sep 2023] Mistral 7B, a high-quality open LLM, was released! Deploy via SkyPilot on any cloud: Mistral docs
[Sep 2023] Case study: Covariant transformed AI development on the cloud using SkyPilot, delivering models 4x faster cost-effectively: read the case study
[Jul 2023] Self-Hosted Llama-2 Chatbot on Any Cloud: example
[Jun 2023] Serving LLM 24x Faster On the Cloud with vLLM and SkyPilot: example, blog post
[Apr 2023] SkyPilot YAMLs for finetuning & serving the Vicuna LLM with a single command!

SkyPilot is a framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability.

SkyPilot abstracts away infra burdens:

Launch dev clusters, jobs, and serving on any infra
Easy job management: queue, run, and auto-recover many jobs

SkyPilot supports multiple clusters, clouds, and hardware (the Sky):

Bring your reserved GPUs, Kubernetes clusters, or 12+ clouds
Flexible provisioning of GPUs, TPUs, CPUs, with auto-retry

SkyPilot cuts your cloud costs & maximizes GPU availability:

Autostop: automatic cleanup of idle resources
Managed Spot: 3-6x cost savings using spot instances, with preemption auto-recovery
Optimizer: 2x cost savings by auto-picking the cheapest & most available infra

SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.

Install with pip:

# Choose your clouds:
pip install -U "skypilot[kubernetes,aws,gcp,azure,oci,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp]"

To get the latest features and fixes, use the nightly build or install from source:

# Choose your clouds:
pip install "skypilot-nightly[kubernetes,aws,gcp,azure,oci,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp]"

Current supported infra (Kubernetes; AWS, GCP, Azure, OCI, Lambda Cloud, Fluidstack, RunPod, Cudo, Paperspace, Cloudflare, Samsung, IBM, VMware vSphere):

SkyPilot

Getting Started

You can find our documentation here.

SkyPilot in 1 Minute

A SkyPilot task specifies: resource requirements, data to be synced, setup commands, and the task commands.

Once written in this unified interface (YAML or Python API), the task can be launched on any available cloud. This avoids vendor lock-in, and allows easily moving jobs to a different provider.

Paste the following into a file my_task.yaml:

resources:
  accelerators: A100:8  # 8x NVIDIA A100 GPU

num_nodes: 1  # Number of VMs to launch

# Working directory (optional) containing the project codebase.
# Its contents are synced to ~/sky_workdir/ on the cluster.
workdir: ~/torch_examples

# Commands to be run before executing the job.
# Typical use: pip install -r requirements.txt, git clone, etc.
setup: |
  pip install "torch<2.2" torchvision --index-url https://download.pytorch.org/whl/cu121

# Commands to run as a job.
# Typical use: launch the main program.
run: |
  cd mnist
  python main.py --epochs 1

Prepare the workdir by cloning:

git clone https://github.com/pytorch/examples.git ~/torch_examples

Launch with sky launch (note: access to GPU instances is needed for this example):

sky launch my_task.yaml

SkyPilot then performs the heavy-lifting for you, including:

Find the lowest priced VM instance type across different clouds
Provision the VM, with auto-failover if the cloud returned capacity errors
Sync the local workdir to the VM
Run the task's setup commands to prepare the VM for running the task
Run the task's run commands

SkyPilot Demo

Refer to Quickstart to get started with SkyPilot.

More Information

To learn more, see our documentation, blog, and community integrations.

Runnable examples:

LLMs on SkyPilot
- Llama 3.2: lightweight and vision models
- Pixtral
- Llama 3.1 finetuning and serving
- GPT-2 via llm.c
- Llama 3
- Qwen
- Databricks DBRX
- Gemma
- Mixtral 8x7B; Mistral 7B (from official Mistral team)
- Code Llama
- vLLM: Serving LLM 24x Faster On the Cloud (from official vLLM team)
- SGLang: Fast and Expressive LLM Serving On the Cloud (from official SGLang team)
- Vicuna chatbots: Training & Serving (from official Vicuna team)
- Train your own Vicuna on Llama-2
- Self-Hosted Llama-2 Chatbot
- Ollama: Quantized LLMs on CPUs
- LoRAX
- QLoRA
- LLaMA-LoRA-Tuner
- Tabby: Self-hosted AI coding assistant
- LocalGPT
- Falcon
- Add yours here & see more in llm/!
Framework examples: PyTorch DDP, DeepSpeed, JAX/Flax on TPU, Stable Diffusion, Detectron2, Distributed TensorFlow, Ray Train, NeMo, programmatic grid search, Docker, Cog, Unsloth, Ollama, llm.c, Airflow and many more (examples/).

Case Studies and Integrations: Community Spotlights

Follow updates:

Read the research:

SkyPilot paper and talk (NSDI 2023)
Sky Computing whitepaper
Sky Computing vision paper (HotOS 2021)
Policy for Managed Spot Jobs (NSDI 2024)

Support and Questions

We are excited to hear your feedback!

For issues and feature requests, please open a GitHub issue.
For questions, please use GitHub Discussions.

For general discussions, join us on the SkyPilot Slack.

Contributing

We welcome all contributions to the project! See CONTRIBUTING for how to get involved.

Release files for trainy-skypilot-nightly 1.0.0.dev20260923

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Source distribution for trainy-skypilot-nightly 1.0.0.dev20260923
File	Size	Uploaded
trainy_skypilot_nightly-1.0.0.dev20260923.tar.gz	954.0 kB	Sep 23, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for trainy-skypilot-nightly 1.0.0.dev20260923
File	Interpreter	ABI	Platform	Reset
trainy_skypilot_nightly-1.0.0.dev20260923-py3-none-any.whl 1.0 MB Sep 23, 2026	Python 3	none	any	Details

Total release size: 2.0 MB

Release files / trainy_skypilot_nightly-1.0.0.dev20260923.tar.gz

Download URL	trainy_skypilot_nightly-1.0.0.dev20260923.tar.gz
Size	954.0 kB
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Release files / trainy_skypilot_nightly-1.0.0.dev20260923-py3-none-any.whl

Download URL	trainy_skypilot_nightly-1.0.0.dev20260923-py3-none-any.whl
Size	1.0 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`27eee2771aa2feb4f14b590620dc9b07cb3594953722e4797bcfe8a8d519da31`
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Upload date	Sep 23, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/asaiacai/zep
Publishing commit: github.com/asaiacai/zep/tree/6af3409936beca77016caba5934ec5d284adba2b

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Subject: trainy_skypilot_nightly-1.0.0.dev20260923-py3-none-any.whl
SHA-256 checksum: 27eee2771aa2feb4f14b590620dc9b07cb3594953722e4797bcfe8a8d519da31 Verifying attestations